Prediction of local structure in proteins using a library of sequence-structure motifs

Abstract

We describe a new method for local protein structure prediction based on a library of short sequence pattern that correlate strongly with protein three-dimensional structural elements. The library was generated using an automated method for ®nding correlations between protein sequence and local structure, and contains most previously described local sequence-structure correlations as well as new relationships, including a diverging type-II b-turn, a frayed helix, and a proline-terminated helix. The query sequence is scanned for segments 7 to 19 residues in length that strongly match one of the 82 patterns in the library. Matching segments are assigned the three-dimensional structure characteristic of the corresponding sequence pattern, and backbone torsion angles for the entire query sequence are then predicted by piecing together mutually compatible segment predictions. In predictions of local structure in a test set of 55 proteins, about 50% of all residues, and 76% of residues covered by high-con®dence predictions, were found in eight-residue segments within 1.4 A Ê of their true structures. The predictions are complementary to traditional secondary structure predictions because they are considerably more speci®c in turn regions, and may contribute to ab initio tertiary structure prediction and fold recognition.

Remote homolog detection using local sequence–structure correlations

“…SVM-pairwise uses the pairwise Smith-Waterman sequence similarity algorithm in place of the gradient vector in the SVM-Fisher method that we described earlier. In contrast, SVM-I-sites encodes the local structure composition of a protein as the sum of I-sites motif confidence scores, 16,17 where each motif defines one feature. After the vectorization step, all of the SVM-based methods will define a similarity score for 2 proteins based on the feature vectors and use that similarity as the kernel of the classifier.…”

“…Local structure motifs were found using the I-sites library of sequence-structure correlations. 17 The I-sites library contains 262 short-sequence patterns that each has a strong correlation with the three-dimensional (3D) structure, locally. Our tests showed that SVM-I-sites was comparable in detection accuracy and more efficient than the state-of-the-art method SVM-pairwise.…”

ProSeg: a database of local structures of protein segments

“…ArchDB is a compilation of structural classifications of loops extracted from known protein structures [15]. I-sites library contains a set of sequence patterns that strongly correlate with protein structure at the local level [16]. LSBSP1 and LSBSP2 contain large sets of sequence profiles for short segments, and these databases have been implemented in the integrated computational system PrISM.1 for predicting local structures [17,18].…”

A Complex Network Approach for the Analysis of Protein Units Similarity Using Structural Alphabet

“…To bridge the current protein structure-function research gap and address anterior questions, many approaches have been proposed for encoding 3D local structural fragments based on Cartesian coordinates into a one-dimensional representation using several letters called the structural alphabet [6]- [13]. The structural alphabet represents advantageous local structures and has been used to 1) compare/analyze 3D structures [14]- [16], 2) predict protein 3D structures from amino acid sequences [6], [9], 3) reconstruct protein backbones [11], and 4) model loops [17].…”

“…The structural alphabet represents advantageous local structures and has been used to 1) compare/analyze 3D structures [14]- [16], 2) predict protein 3D structures from amino acid sequences [6], [9], 3) reconstruct protein backbones [11], and 4) model loops [17]. In addition, given that local structures are generally more evolutionary conserved than amino acid sequences, a series of research has been developed to explore protein structures [18].…”